Exploring Sleep Challenges and Interventions in Children with a Vision Impairment: A Scoping Review
Bibliographic record
Abstract
Background/Objectives: Sleep problems are highly prevalent among children with vision impairment and can negatively affect physical, emotional, and cognitive development. There is a need to identify and evaluate effective interventions in this population. This scoping review aimed to map the range of sleep challenges experienced by these children and to summarise the interventions evaluated to date. Methods: Systematic searches were performed in Embase, Medline, and Web of Science Core Collection. Screening was completed in Covidence, and data extraction and descriptive analysis were conducted using Microsoft Excel (version 2510) and IBM SPSS Statistics (version 30). Narrative synthesis was used to summarise findings. Results: Fifteen studies were included, over half of which were case reports. The vast majority (14/15) were conducted in high-income countries, leaving a significant evidence gap for low- and middle-income settings. Reported sleep challenges included delayed sleep onset, non-24-h sleep–wake disorder, early morning waking, and fragmented sleep. Interventions were predominantly pharmacological (11/15), with melatonin the most frequently evaluated. Across studies, melatonin demonstrated short-term effectiveness in improving sleep latency, duration, and parent-reported quality, though prescribing practices, dosages, and availability varied. Other pharmacological options, such as tasimelteon and vitamin B12, were rarely reported. Non-pharmacological strategies were evaluated in only a small number of studies and included behavioural interventions, structured routines, and activity-based therapies. These showed potential benefit but remain under-researched. Conclusions: Overall, the evidence base is small, heterogeneous, and methodologically limited. Further research is needed to develop and carefully test non-pharmacological approaches, and to compare them directly with pharmacological treatments, to provide families and clinicians with effective and sustainable options.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".